The raw data provided by radar (a measurement of range and azimuth in the radar coordinate system) is subject to random noise and systematic errors (aka registration errors). Before radar targets can be used by the tracking and display functions of Air Traffic Control (ATC) systems, registration errors must be removed with as much accuracy as possible. In multi-sensor environments failure to align the reference frames could result in unphysical track discontinuities and degraded surveillance accuracy to levels even lower than any single sensor. Legacy systems utilize methodologies to address registration errors between radars such that corrected reports from multiple radars each report an aircraft to be at the same position. The introduction of ADS-B (Automatic Dependent Surveillance—Broadcast) gives rise to a need to register ADS-B target positions with radar positions in order to support safe separation of ADS-B equipped aircraft from non ADS-B equipped aircraft. In addition, ADS-B opens the possibility for significant accuracy improvements in the determination of registration parameters.
Air traffic controllers maintain aircraft safely separated with the help of targets rendered on a situation display as well as other tools. In a multi-sensor environment (e.g., combining various radars and ADS-B) the positional data displayed to the controller will show path discontinuities when there are residual registration correction errors. Inaccuracies in registration correction will also have a detrimental impact in the accuracy of the tracker and subsequently in the functions that depend on tracker outputs (such as the tactical conflict alert function).
The legacy solution to the registration bias problem was developed based on a radar-pair system. Targets from aircraft flying in a region where the coverage of two radars overlaps (at least partially) are time-aligned and the distance between them computed. After collecting a large sample of such targets the random component in the computed distance is reduced (tends to cancel out or reach negligible levels for sufficiently large samples); the remaining value is an indication of uncorrected bias (azimuth and range separately). All of these algorithms are related to the Maximum Likelihood (ML) problem (i.e. obtain the best ‘model’ parameters possible given the observed data, where the ‘model’ here is a simple additive bias to the azimuth and the range). Depending on the level of sophistication, these algorithms are formulated in terms of a Least Squares (LS) problem (only variances are used), or a Generalized Least Squares (GLS) problem (full covariance matrix is used).
However, registration correction algorithms that work with radar-pairs are not readily usable with ADS-B surveillance sources. Leaving data format incompatibilities aside, possible extensions of the two-radar algorithms to use ADS-B sources by treating the ADS-B as data of better quality fail in two respects: a) most of the algorithms do not support sensors with large differences in accuracy and, most importantly, b) even if they incorporated weights to the measurements based on sensor accuracy, in a multi-sensor environment the registration solutions can potentially be unstable exhibiting oscillations (‘ringing’) when a 3rd sensor is introduced. To exemplify: when the sensor pair A-B is used the registration solution for B is ‘high’, but when the algorithm is run for the pair B-C the solution for B becomes ‘low’. An unstable, oscillating solution is observed when alternating between A and C to find registration corrections for B.
A commonly used algorithm in major ATC systems (HCS, ERAM, etc) is the 2-radar 4-equation method (R2E4 legacy registration), which is a Least Squares minimization of the distance between pairs of reports coming from two radars and using a large collection of time aligned common targets. These algorithms rely on collecting two separate samples of targets from two regions located to each side of the line joining the radar centers. The need to have two independent samples collected in these two separate regions is dictated by the choice of LS problem that couples the equations containing the 4 registration parameters (range and azimuth for two radars). In addition to the two problems described above, the R2E4 introduces the additional burden of the need to have separate collections in two regions, which could limit (or exclude altogether) its usability in areas of low traffic. Another drawback of the legacy registration algorithm is the need for a single-sensor tracker implemented within the registration function that provides accurate time extrapolated positions to allow for time-coincident comparisons of target positions. To improve accuracy of the time alignment of targets, in some implementations a maneuver detector filters out maneuvering targets (adding algorithm complexity).